pasted from history

app.py

@@ -1,30 +1,35 @@
-__all__ = ['is_real', 'learn', 'virtual staging', 'classify_image', 'categories', 'image', 'label', 'examples', 'intf']
 import pathlib
-
+temp = pathlib.PosixPath
+pathlib.PosixPath = pathlib.WindowsPath
 #|export
 #fastai has to be available, i.e. fastai folder
 from fastai.vision.all import *
 import gradio as gr
+import pickle
+
+with open('./model.pkl', 'rb') as f:
+    model = pickle.load(f)
 
 def is_real(x): return x[0].isupper()
 
-# Cell
+#|export
 learn = load_learner('model.pkl')
 
 #|export
 categories =('Virtual Staging','Real')
 
 def classify_image(img):
-    pred,idx,probs = learn.predict(img)
+    pred,idx,probs = learn.predict(im)
     return dict(zip(categories,map(float,probs)))
 
 #*** We have to cast to float above because KAGGLE does not return number on the answer it returns tensors, and Gradio does not deal with numpy so we have to cast to float
 
 #|export
-
+#import gradio as gr
+import gradio as gr
 image = gr.inputs.Image(shape=(192,192))
 label = gr.outputs.Label()
 examples = ['virtual.jpg','real.jpg']
 
 intf = gr.Interface(fn=classify_image,inputs=image,outputs=label,examples=examples)
-intf.launch(inline=False)
+intf.launch(inline=False)